Many clinical conditions are characterized by maladaptive behavioral control, such as eating disorders, addictions, obsessive-compulsive disorders, among others. Greater success in treating such conditions will come from a better understanding of the systems that provide control over motivationally based behavior. Our research supports a dual function model of the basolateral amygdala (BLA) in associative learning. Across a number of behavioral domains, BLA-dependent learning promotes action that is appropriate to the past experience of the organism, bringing species typical behavioral systems under strong motivational control by learned cues. Through associative learning such cues gain strength in the control of behavioral systems via outputs to hypothalamus and brainstem. At the same time, through its interconnections with cortical systems, BLA serves a second function in mapping predictive relationships between events, which are used in goal-directed behavior. That function of BLA makes a critical contribution to me top-down control of action, even in the face of strong response tendencies generated by lower-level learning. In our behavioral model for the first function of BLA, learning drives food consumption, such that conditioned cues, which signaled the availability of food when animals were hungry, augment food consumption in subsequent tests when satiation and replete energy stores. The proposed studies will define the contribution of components in this circuitry to acquisition, maintenance, and expression of conditioned potentiation of feeding. We will test the hypothesis that output to hypothalamus acts on the internal coding provided by satiety signals, and will examine the variable control of aversively conditioned cues on feeding, either suppressing or augmenting eating, as an output function of amygdala-hypothalamic circuits. To address the second BLA function, we will build on our finding that BLA integrity is essential for the development of cue-activated encoding of predicted outcomes in the orbitofrontal cortex (OFC) in an analysis of associatively activated flavor representations within a network of BLA-OFC-gustatory cortex (GC). The associative mapping of predictable events provides a template for flexible behavioral control, in which the value of a reinforcing event can be rapidly modified and continuously updated. We propose studies to determine the encoding properties of neurons in specific cortical regions and interactions among components of the network during learning and retrieval of associative information. We will further examine a basis for rapid top-down adjustments that occur in reinforcer devaluation paradigms. That work will examine specific neural coding in the cortical network that reflects changes in the value of predicted outcomes that can be used to control prepotent responses established by prior learning. A notable feature of the research on the neurobiology of these functions is the correspondence that exists in different species of laboratory animals (rodents and non-human primates) and the applicability of those findings to humans. As such, the work has important implications for clinical disorders of impulse and behavioral control.